Smart Sustainable Production and Distribution Network Model for City Multi-Floor Manufacturing Clusters

This study focuses on management ways within a city multi-floor manufacturing cluster (MFMC). The application of MFMC in megapolises is closely related to the problem of urban spatial development and the problem of matching transport and logistics services. The operation of the MFMC depends on the efficiency of production and transport management considering technical, economic, end environmental factors. Therefore, conditions affecting decision-making in the field of production planning by MFMCs and accompanying transports within the agglomeration area with the use of the production-service platform were presented. Assumptions were created for the decision model, allowing for the selection of partners within the MFMC to execute the production order. A simplified decision model using the Hungarian algorithm was proposed, which was verified with the use of test data. The model is universal for material flow analysis and is an assessments basis for smart sustainable supply chain decision-making and planning. Despite the narrowing of the scope of the analysis and the simplifications applied, the presented model using the Hungarian algorithm demonstrated its potential to solve the problem of partner selection for the execution of the contract by MFMC.

Formation Control of a Multi-Autonomous Underwater Vehicle Event-Triggered Mechanism Based on the Hungarian Algorithm

Among the key technologies of Autonomous Underwater Vehicle (AUV) leader–follower formations control, formation reconfiguration technology is one of the main technologies to ensure that multiple AUVs successfully complete their tasks in a complex operating environment. The biggest drawback of the leader–follower formations technology is the failure of the leader and the excessive communication pressure of the leader. Aiming at the problem of leader failure in multi- AUV leader–follower formations, the Hungarian algorithm is used to reconstruct the failed formation with a minimum cost, and the improvement of the Hungarian algorithm can solve the problem of a non-standard assignment. In order to solve the problem of an increased leader communication task after formation reconfiguration, the application of an event-triggered mechanism (ETM) can reduce unnecessary and useless communication, while the efficiency of the ETM can be improved through increasing the event-triggered conditions of the sampling error threshold. The simulation results of multi-AUV formation control show that the Hungarian algorithm proposed in this paper can deal with the leader failure in the multi-AUV leader–follower formation, and the ETM designed in this paper can reduce about 90% of the communication traffic of the formation which also proves the highly efficient performance of the improved ETM in the paper.

An Exact Algorithm for Task Allocation of Multiple Unmanned Surface Vehicles with Minimum Task Time

Task allocation of unmanned surface vehicles (USVs) with low task cost is an important research area which assigns USVs from starting points to different target points to complete tasks. Most of the research lines of task allocation are using heuristic algorithms to obtain suboptimal solutions to reduce both the max task cost and total task cost. In practice, reducing the maximum is more important to task time, which is from the departure of USVs to the last USV arriving at the designated position. In this paper, an exact algorithm is proposed to minimize the max task time and reduce the total task time based on the Hungarian algorithm. In this algorithm, task time is composed of the travel time along the planned path and the turning time at initial and target points. The fast marching square method (FMS) is used to plan the travel path with obstacle avoidance. The effectiveness and practicability of the proposed algorithm are verified by comparing it with the Hungarian algorithm (HA), the auction algorithm (AA), the genetic algorithm (GA) and the ant colony optimization algorithm (ACO). The results of path planning and task allocation are displayed in the simulation.

The utility of clusters and a Hungarian clustering algorithm

Implicit in the k–means algorithm is a way to assign a value, or utility, to a cluster of points. It works by taking the centroid of the points and the value of the cluster is the sum of distances from the centroid to each point in the cluster. The aim in this paper is to introduce an alternative way to assign a value to a cluster. Motivation is provided. Moreover, whereas the k–means algorithm does not have a natural way to determine k if it is unknown, we can use our method of evaluating a cluster to find good clusters in a sequential manner. The idea uses optimizations over permutations and clusters are set by the cyclic groups; generated by the Hungarian algorithm.

A Fast Dynamic Assignment Algorithm for Solving Resource Allocation Problems

The assignment problem is one of the fundamental problems in the field of combinatorial optimization. The Hungarian algorithm can be developed to solve various assignment problems according to each criterion. The assignment problem that is solved in this paper is a dynamic assignment to find the maximum weight on the resource allocation problems. The dynamic characteristic lies in the weight change that can occur after the optimal solution is obtained. The Hungarian algorithm can be used directly, but the initialization process must be done from the beginning every time a change occurs. The solution becomes ineffective because it takes up a lot of time and memory. This paper proposed a fast dynamic assignment algorithm based on the Hungarian algorithm. The proposed algorithm is able to obtain an optimal solution without performing the initialization process from the beginning. Based on the test results, the proposed algorithm has an average time of 0.146 s and an average memory of 4.62 M. While the Hungarian algorithm has an average time of 2.806 s and an average memory of 4.65 M. The fast dynamic assignment algorithm is influenced linearly by the number of change operations and quadratically by the number of vertices.